The validity of the QSAR equation and the associated pharmacophoric models were . GPCRs: An update on structural approaches to drug discovery. Article. Quantitative Structure Activity Relationship (QSAR) models are an established means of Ligand-Based Computer-Aided Drug Discovery. Insilico Modelling of Quantitative Structure-Activity Relationship of Pgi50 and Theoretical Chemistry; Medicinal & Pharmaceutical Chemistry.
The developed QSAR model was used to discover some compounds as new lead photosensitizers from this external test set. QSAR, photodynamic therapy, photosensitizer, porphyrin, IC50 half maximal inhibitory concentration 1.
Quantitative structure–activity relationship
Introduction Cancer is a dangerous disease in which cells grow and divide beyond their normal limits. Currently, the major treatments for cancer include surgery, chemotherapy, and radiation [ 1 ]. However, high incidences of undesirable side effects have prompted researchers to search for safer and more effective treatments. Photodynamic therapy PDT provides an alternative treatment for cancer with relatively low side effects [ 2 ]. This treatment uses the combined effects of light and light activated toxic drugs or photosensitizers to target tumor cells.
Photosensitizers are chemical compounds that could be excited by light of a specific wavelength [ 3 ], often with visible or near infrared light. A photosensitive drug absorbs photons which alter the drugs into an excited state.
These excited drugs then pass their energy to oxygen to form free radicals singlet oxygen which oxidize cellular structures [ 4 — 7 ]. Oxidative damage caused by the free radicals exceeds a threshold level causing the cells to die. Photofrin and other early photosensitizers often referred to as first generation sensitizershave properties that make them less than ideal for use in clinical PDT settings.
First generation photosensitizers have several serious drawbacks in that they are not specific to cancer cells, but also tend to accumulate in normal tissues [ 7 ]. This means that not only the cancer cells, but also normal cells could be damaged by the treatment.
In addition, first generation photosensitizers do not discharge rapidly from the human body. Hence, patients receiving photofrin treatment must stay out of the sun for at least a month following treatment [ 8 ]. In addition, larger and deep-seated tumors cannot normally be treated with these agents.
Much work has been done to develop new photosensitizers second generation to improve the pharmacokinetics and physical properties of the first generation photosensitizers [ 9 ]. Important objectives for scientists remain to develop new photosensitizers of pure compounds which are activated strongly by red light above nm [ 10 ].
Quantitative structure–activity relationship - Wikipedia
Many QSAR approaches have been used to search for new photosensitizing agents for cancer therapy. In this context FB-QSAR proves to be a promising strategy for fragment library design and in fragment-to-lead identification endeavours.
The training set needs to be superimposed aligned by either experimental data e. It uses computed potentials, e. It examined the steric fields shape of the molecule and the electrostatic fields  which were correlated by means of partial least squares regression PLS.
The created data space is then usually reduced by a following feature extraction see also dimensionality reduction. The following learning method can be any of the already mentioned machine learning methods, e.
A label or response is assigned to each set corresponding to the activity of the molecule, which is assumed to be determined by at least one instance in the set i. This approach is different from the 3D-QSAR approach in that the descriptors are computed from scalar quantities e.
An example of this approach is the QSARs developed for olefin polymerization by half sandwich compounds. Data mining approach[ edit ] Computer SAR models typically calculate a relatively large number of features. Because those lack structural interpretation ability, the preprocessing steps face a feature selection problem i. Feature selection can be accomplished by visual inspection qualitative selection by a human ; by data mining; or by molecule mining.
A typical data mining based prediction uses e. Molecule mining approaches, a special case of structured data mining approaches, apply a similarity matrix based prediction or an automatic fragmentation scheme into molecular substructures. Furthermore, there exist also approaches using maximum common subgraph searches or graph kernels.
Matched molecular pair analysis Typically QSAR models derived from non linear machine learning is seen as a "black box", which fails to guide medicinal chemists. Recently there is a relatively new concept of matched molecular pair analysis  or prediction driven MMPA which is coupled with QSAR model in order to identify activity cliffs. QSARs are being applied in many disciplines, for example: Any QSAR modeling should ultimately lead to statistically robust and predictive models capable of making accurate and reliable predictions of the modeled response of new compounds.
For validation of QSAR models, usually various strategies are adopted: The success of any QSAR model depends on accuracy of the input data, selection of appropriate descriptors and statistical tools, and most importantly validation of the developed model. Validation is the process by which the reliability and relevance of a procedure are established for a specific purpose; for QSAR models validation must be mainly for robustness, prediction performances and applicability domain AD of the models.
For example, leave one-out cross-validation generally leads to an overestimation of predictive capacity.